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Abstract Stroke, the second leading cause of death and disability, causes massive cell death in the brain followed by secondary inflammatory injury initiated by disease associated molecular patterns released from dead cells. Nonetheless, the evidence regarding the causal relationship between inflammatory cytokines and stroke subtypes is obscure. To leverage large scale genetic association data to investigate the interplay between circulating cytokines and stroke, we adopted a two-sample bi-directional Mendelian randomization (MR) analysis. Firstly, we performed a forward MR analysis to examine the associations of genetically determined 31 cytokines with 6 stroke subtypes. Secondly, we conducted a reverse MR analysis to check the associations of 6 stroke subtypes with 31 cytokines. In the forward MR analysis, genetic evidence suggests that 21 cytokines were significantly associated with certain stroke subtype risk with |β| ranging from 1.90 × 10−4 to 0.74. In the reverse MR analysis, our results found that five stroke subtypes (intracerebral hemorrhage (ICH), large artery atherosclerosis ischemic stroke (LAAS), lacunar stroke (LS), cardioembolic ischemic stroke (CEI), small-vessel ischemic stroke (SV)) caused significantly changes in 16 cytokines with |β| ranging from 1.08 × 10−4 to 0.69. In particular, those five stroke subtypes were statistically significantly associated with C-reactive protein (CRP). In addition, ICH, LAAS, LS and SV were significantly correlated with vascular endothelial growth factor (VEGF), while LAAS, LS, CEI and SV were significantly related to fibroblast growth factor (FGF). Moreover, integrated bi-directional MR analysis, these factors (IL-3Rα, IL-6R, IL-6Rα, IL-1Ra, insulin-like growth factor-1(IGF-1), IL-12Rβ2) can be used as predictors of some specific stroke subtypes. As well as, IL-16 and C–C motif chemokine receptor 7 (CCR7) can be used as prognostic factors of stroke. Our findings prognostic identify potential pharmacological opportunities, including perturbation of circulating cytokines for both predicting stroke risk and post stroke treatment effects. As we conducted a comprehensive search and analysis of stroke subtype and cytokines in the existing publicly available GWAS database, the results have good population-generalizability.

Vitamin D deficiency has emerged as a significant concern in public health due to its potential association with various metabolic disorders. This study aimed to investigate the relationship between serum vitamin D levels and the susceptibility to hyperlipidemia among adults. Using a multi-stage sampling approach, we recruited a cohort of 2072 eligible individuals aged over 18 years. Serum 25-hydroxyvitamin D [25(OH)D] levels were measured alongside glucolipid metabolic markers, and comprehensive demographic and physical data were collected. The cohort exhibited a hyperlipidemia prevalence of 42.18%, with 19.88% demonstrating vitamin D deficiency. Notably, 23.68% of individuals with vitamin D deficiency also presented hyperlipidemia. Statistical analysis revealed a significantly higher prevalence of hyperlipidemia among those with vitamin D deficiency compared to those with sufficient levels (23.68% vs. 17.11%, P < 0.05). After adjusting for various factors including age, geographical region, exercise status, BMI, fasting glucose level, and blood pressure, lower serum 25(OH)D concentrations were found to significantly increase the risk of hyperlipidemia (Odds Ratio [OR] = 1.41; 95% CI: 1.057, 1.885; P < 0.05). Further stratification of the hyperlipidemic cohort revealed that vitamin D deficiency was associated with 1.459- and 1.578-times higher risks for total cholesterol and triglyceride abnormalities, respectively, compared to those with sufficient vitamin D levels. Moreover, each 10 ng/mL decrease in serum vitamin D level corresponded to an increased risk of total cholesterol (OR = 0.82; 95% CI: 0.728, 0.974; P < 0.05) and triglyceride abnormalities (OR = 0.79; 95% CI: 0.678, 0.927; P < 0.05). However, there were no significant differences observed between vitamin D-sufficient and−deficient groups regarding Low-Density Lipoprotein (LDL) and High-Density Lipoprotein (HDL) abnormalities. These findings underscore the potential role of serum vitamin D deficiency as an independent risk factor contributing to the elevated prevalence of hyperlipidemia in the adult population.

BackgroundTraditional Chinese opera, such as Pingju, requires actors to master sophisticated performance skills and cultural knowledge, potentially influencing brain function. This study aimed to explore the effects of long-term opera training on the dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC).MethodsTwenty professional well-trained Pingju actors and twenty demographically matched untrained subjects were recruited. Resting-state functional magnetic resonance imaging (fMRI) data were collected to assess dALFF differences in spontaneous regional brain activity between the actors and untrained participants. Brain regions with altered dALFF were selected as the seeds for the subsequent dFC analysis. Statistical comparisons examined differences between groups, while correlation analyses explored the relationships between dALFF and dFC, as well as the associations between these neural measures and the duration of Pingju training.ResultsCompared with untrained subjects, professional Pingju actors exhibited significantly lower dALFF in the right lingual gyrus. Additionally, actors showed increased dFC between the right lingual gyrus and the bilateral cerebellum, as well as between the right lingual gyrus and the bilateral midbrain/red nucleus/thalamus, compared with untrained subjects. Furthermore, a negative correlation was found between the dALFF in the right lingual gyrus and its dFC, and a significant association was found between dFC in the bilateral midbrain/red nucleus/thalamus and the duration of Pingju training.ConclusionLong-term engagement in Pingju training induces neuroplastic changes, reflected in altered dALFF and dFC. These findings provide evidence for the interaction between artistic training and brain function, highlighting the need for further research into the impact of professional training on cognitive functions.

IntroductionMolting is induced in commercial laying hens to rejuvenate the reproductive system and increase egg production. However, this process causes stress and reduces bird health and performance.MethodsThe experiment was conducted to study the effect of multi probiotics and vitamin additives on induced molting in 240 ISA Brown hens. Hens were randomly divided into four groups receiving probiotic and vitamin additives (I–IV) during different period of molting. During the whole molting process, the laying performance indexes such as egg laying rate, egg quality, ovary weight and oviduct lengths were measured, and the spleen index, serum immunoglobulin, immune response of NDV and AIV vaccine were monitored.ResultsMolted hens resumed 50% egg production in just 37 days, with 1.62% mortality. Egg quality such as egg weight, yolk color, Haugh unit, eggshell strength and protein height were significantly improved. After the second production peak, the reproductive organs and immune organs returned to normal, and the immune antibody titer of NDV vaccine increased significantly.DiscussionMolting with probiotic and vitamin additives improve the laying performance and egg quality, reduce mortality, significantly improve immune function and vaccine titer, and help to enhance disease resistance and maintain production performance of aged laying hens.

Abstract Background Building on our prior work that RNA alternative splicing modulates the druggability of kinase fusions, this study probes the clinical significance of sole reciprocal fusions. These rare genomic arrangements, despite lacking kinase domains at the DNA level, demonstrated potential RNA‐level druggability in sporadic cases from our prior research. Methods Utilizing the large‐scale multicenter approach, we performed RNA sequencing and clinical follow‐up to evaluate a broad spectrum of kinase fusions, including ALK, ROS1, RET, BRAF, NTRK, MET, NRG1, and EGFR, in 1943 patients. Results Our findings revealed 51 instances (2.57%) of sole reciprocal fusions, predominantly in lung (57%), colorectal (14%), and glioma (10%) cancers. Comparative analysis with an MSKCC cohort confirmed the prevalence in diverse cancer types and identified unique fusion partners and chromosomal locales. Cross‐validation through RNA‐NGS and FISH authenticated the existence of functional kinase domains in subsets including ALK, ROS1, RET, and BRAF, which correlated with positive clinical responses to targeted kinase inhibitors (KIs). Conversely, fusions involving EGFR, NRG1, and NTRK1/2/3 generated nonfunctional transcripts, suggesting the need for alternative therapeutic interventions. Conclusion This inaugural multicenter study introduces a novel algorithm for detecting and treating sole reciprocal fusions in advanced cancers, expanding the patient population potentially amenable to KIs.

ABSTRACTLand use/cover change is a major cause of ecological degradation. Reliable LUCC data are essential for evaluating habitat quality. The current method of surface cover classification based on the convolutional neural networks (CNNs) is usually a local spatial operation using a regular convolutional kernel, which ignores the correlation between adjacent image elements. This paper proposes a combination network with two branches, branch 1 uses the K-nearest neighbor clustering algorithm to construct superpixels and then uses the data transformation module to construct a graph attention network (GAT); branch 2 constructs the CNN using attention and residual modules to obtain the spatial and higher-order semantic information of the images. Finally, the features are fused using weighted fusion, and a classification map with less point noise and greater consistency with the real surface coverage is obtained. The classification results of this network are better than those of the other competitive methods. In addition, the urbanization of Sanya has resulted in significant habitat degradation. A good fit ([Formula: see text] in 2020 = 0.639) between habitat quality (HQ) and natural and socioeconomic factors was observed in Sanya. Natural factors are more relevant to HQ than socioeconomic factors and vary spatially.

Major depressive disorder (MDD) is a debilitating mental health condition that poses significant risks and burdens. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a promising tool in investigating the neural mechanisms underlying MDD. However, a comprehensive bibliometric analysis of resting-state fMRI in MDD is currently lacking. Here, we aimed to thoroughly explore the trends and frontiers of resting-state fMRI in MDD research. The relevant publications were retrieved from the Web of Science database for the period between 1998 and 2022, and the CiteSpace software was employed to identify the influence of authors, institutions, countries/regions, and the latest research trends. A total of 1501 publications met the search criteria, revealing a gradual increase in the number of annual publications over the years. China contributed the largest publication output, accounting for the highest percentage among all countries. Particularly, the University of Electronic Science and Technology of China, Capital Medical University, and Harvard Medical School were identified as key institutions that have made substantial contributions to this growth. Neuroimage, Biological Psychiatry, Journal of Affective Disorders, and Proceedings of the National Academy of Sciences of the United States of America are among the influential journals in the field of resting-state fMRI research in MDD. Burst keywords analysis suggest the emerging research frontiers in this field are characterized by prominent keywords such as dynamic functional connectivity, cognitive control network, transcranial brain stimulation, and childhood trauma. Overall, our study provides a systematic overview into the historical development, current status, and future trends of resting-state fMRI in MDD, thus offering a useful guide for researchers to plan their future research.

Powered by optical energy, photocatalytic reduction for fuel production promises to be an ideal long-term solution to a number of key energy challenges. Photocatalysts with enhanced light absorption, fast electron/hole separation rates, and exposed activity sites are essential to improve photocatalytic efficiency. Semiconductors are constrained by their own intrinsic properties and have limited performance in photocatalysis, but defect engineering provides an opportunity to modulate the physical and chemical properties of semiconductors. Defect engineering has been shown to be effective in regulating electron distribution and accelerating photocatalytic kinetics during photocatalysis. This review introduces the definition and categorization of defects, then explains the main effects of defect engineering on photoabsorption, carrier separation/migration, and surface reduction reactions. We then review the milestones in the design of defect-engineered photocatalysts for key chemical reactions, including hydrogen evolution, CO2 reduction, and N2 reduction, and tabulate their respective effects on catalytic performance. Finally, we provide insights and perspectives on the challenges and potential of defect engineering for photoreduction reactions.

Accurate extraction of winter wheat and its planted area holds significance for agricultural research and government real-time food monitoring. Traditional machine learning methods often demand extensive data and corresponding labels for training in cross-domain classification problems. The heterogeneity of land cover types causes an uneven distribution of samples, leading to unsatisfactory results when these methods are applied directly to other regions. This article introduces a two-branch Graph Convolutional Fusion CNN network incorporating dynamic weighted stratified loss to address these challenges. To reduce the weight of losses generated by easily classified pixels, this loss function adds task masks and category dynamic weights to the cross-entropy loss. Dual branching merges global insights from graph convolutional networks with local emphasis from convolutional neural networks. It enhances the handling of cross-domain classification problems. The first branch introduces an adaptive mechanism and applies it to the graph's adjacency matrix to enhance the model's adaptability to different domain graph structures. The second branch alleviates the oversmoothing problem of edge clustering caused by graph convolution and handles multiscale and spectral information more efficiently. The experimental results showed that the proposed method achieved 99.98% accuracy and good classification results on the Zhoukou dataset. The zero-shot cross-domain prediction on the Suixian dataset achieved 96.11% accuracy. Ultimately, the entire winter wheat planting area of Shangqiu City was extracted with an accuracy of 91.92%. Numerous experiments and practical applications confirm that the proposed method is feasible and effective for winter wheat cross-domain extraction.

Abstract The security of smart contract has always been one of the significant problems in blockchain. As shown in previous studies, vulnerabilities in smart contracts can lead to unpredictable losses. With the rapid growth of the number of smart contracts, more and more data driven detection technologies based on machine learning have been proposed. However, some state‐of‐the‐art approaches mainly rely on the source code of smart contract. These methods are limited by the openness of the source code and the version of the programming language. To address this problem, we propose a novel vulnerability detection method based on transformer by constructing the control flow graph (CFG) of smart contracts operation codes (opcodes), which shields the difference of various versions of program language. Extensive experiments are conducted to evaluate the effectiveness of the proposed method on the authors' own collected dataset. The experimental results show that the proposed method achieves 94.36% accuracy in vulnerability detection, which performs better than other state‐of‐the‐art methods.

The target is often obstructed by obstacles with the dynamic tracking scene, leading to a loss of target information and a decrease in tracking accuracy or even complete failure. To address these challenges, we leverage the capabilities of Geiger-mode Avalanche Photodiode (GM-APD) LiDAR to acquire both intensity images and point cloud data for researching a target tracking method that combines the fusion of intensity images and point cloud data. Building upon Kernelized correlation filtering (KCF), we introduce Fourier descriptors based on intensity images to enhance the representational capacity of target features, thereby achieving precise target tracking using intensity images. Additionally, an adaptive factor is designed based on peak sidelobe ratio and intrinsic shape signature to accurately detect occlusions. Finally, by fusing the tracking results from Kalman filter and KCF with adaptive factors following occlusion detection, we obtain location information for the central point of the target. The proposed method is validated through simulations using the KITTI tracking dataset, yielding an average position error of 0.1182m for the central point of the target. Moreover, our approach achieves an average tracking accuracy that is 21.67% higher than that obtained by Kalman filtering algorithm and 7.94% higher than extended Kalman filtering algorithm on average.

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

The subsurface defects (SSD) of optical elements have a significant impact on the service life and laser damage threshold of the inertial confinement nuclear fusion optical system. Due to the optical diffraction limit, fluorescence microscopy has limited image resolution for detecting SSD in optical elements, making it challenging to meet the requirements for precision detection. A super-resolution method is proposed in this paper for detecting SSD in optical elements based on the fluorescence polarization characteristics of CdSe/ZnS quantum dots fluorescence dichroism. By enhancing the imaging sparsity of adjacent fluorophores through a polarization modulated excitation laser and utilizing the fluorescence polarization modulation algorithm, the resolution is achieved beyond the optical diffraction limit. The results demonstrate that the proposed method in this paper can obtain super-resolution images of SSD in optical elements with at least a two-fold increase in lateral resolution. This approach effectively improves detection accuracy for SSD and holds significant guiding significance. In addition, it also has application value for assessing the quality of high-precision optical elements.

Background Over 50% of patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) receiving CD19-targeted chimeric antigen receptor (CAR19) T-cell therapy fail to achieve durable remission. Early identification of relapse or progression remains a significant challenge. In this study, we prospectively investigate the prognostic value of dynamic circulating tumor DNA (ctDNA) and track genetic evolution non-invasively, for the first time in an Asian population of r/r patients undergoing CAR19 T-cell therapy.Methods Longitudinal plasma samples were prospectively collected both before lymphodepletion and at multiple timepoints after CAR19 T-cell infusion. ctDNA was detected using a capture-based next-generation sequencing which has been validated in untreated LBCL.Results The study enrolled 23 patients with r/r LBCL and collected a total of 101 ctDNA samples. Higher pretreatment ctDNA levels were associated with inferior progression-free survival (PFS) (p=0.031) and overall survival (OS) (p=0.023). Patients with undetectable ctDNA negative (ctDNA–) at day 14 (D14) achieved an impressive 3-month complete response rate of 77.8% vs 22.2% (p=0.015) in patients with detectable ctDNA positive (ctDNA+), similar results observed for D28. CtDNA– at D28 predicted significantly longer 1-year PFS (90.9% vs 27.3%; p=0.004) and OS (90.9% vs 49.1%; p=0.003) compared with patients who remained ctDNA+. Notably, it is the first time to report that shorter ctDNA fragments (

ROS proto‐oncogene 1, receptor tyrosine kinase (ROS1) rearrangements are a crucial therapeutic target in non‐small cell lung cancer (NSCLC). However, there is limited comprehensive analysis of the molecular patterns of ROS1 fusions. This study aimed to address this gap by analysing 135 ROS1 fusions from 134 Chinese NSCLC patients using next‐generation sequencing (NGS). The fusions were categorized into common and uncommon based on their incidence. Our study revealed, for the first time, a unique distribution preference of breakpoints within ROS1, with common fusions occurring in introns 31–33 and uncommon fusions occurring in introns 34 and 35. Additionally, we identified previously unknown breakpoints within intron 28 of ROS1. Furthermore, we identified a close association between the distribution patterns of fusion partners and breakpoints on ROS1, providing important insights into the molecular landscape of ROS1 fusions. We also confirmed the presence of inconsistent breakpoints in ROS1 fusions between DNA‐based NGS and RNA‐based NGS through rigorous validation methods. These inconsistencies were attributed to alternative splicing resulting in out‐of‐frame or exonic ROS1 fusions. These findings significantly contribute to our understanding of the molecular characteristics of ROS1 fusions, which have implications for panel design and the treatment of NSCLC patients with ROS1 rearrangements.

Aiming at the problem that the range profile of the current array lidar has a low resolution and contains few target details and little edge information, a super-resolution reconstruction method based on projection onto convex sets (POCS) combining the Lucas–Kanade (LK) optical flow method with a Gaussian pyramid was proposed. Firstly, the reference high-resolution range profile was obtained by the nearest neighbor interpolation of the single low-resolution range profile. Secondly, the LK optical flow method was introduced to achieve the motion estimation of low-resolution image sequences, and the Gaussian pyramid was used to perform multi-scale correction on the estimated vector, effectively improving the accuracy of motion estimation. On the basis of data consistency constraints, gradient constraints were introduced based on the distance value difference between the target edge and the background to enhance the reconstruction ability of the target edge. Finally, the residual between the estimated distance and the actual distance was calculated, and the high-resolution reference range profile was iteratively corrected by using the point spread function according to the residual. Bilinear interpolation, bicubic interpolation, POCS, POCS with adaptive correction threshold, and the proposed method were used to reconstruct the range profile of the datasets and the real datasets. The effectiveness of the proposed method was verified by the range profile reconstruction effect and objective evaluation index. The experimental results show that the index of the proposed method is improved compared to the interpolation method and the POCS method. In the redwood-3dscan dataset experiments, compared to the traditional POCS, the average gradient (AG) of the proposed method is increased by at least 8.04%, and the edge strength (ES) is increased by at least 4.84%. In the real data experiments, compared to the traditional POCS, the AG of the proposed method is increased by at least 5.85%, and the ES is increased by at least 7.01%, which proves that the proposed method can effectively improve the resolution of the reconstructed range map and the quality of the detail edges.

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment.

Abstract Depressive disorder prevalence in patients with schizophrenia has been reported to be 40%. People with low socioeconomic status (SES) are more likely to suffer from schizophrenia and major depressive disorder (MDD). However, the causal relationship between schizophrenia and depression and the potential mediating role of SES remains unclear. Two-sample Mendelian randomization (MR) analyses were conducted to explore the bidirectional causal relationship between schizophrenia and MDD with the largest sample size of European ancestry from public genome-wide association studies (sample size ranged from 130,644 to 480,359). Inverse variance weighted (IVW) method was used as the primary analysis, and several canonical MR methods were used as validation analyses. The mediating role of SES (educational years, household income, employment status, and Townsend deprivation index) was estimated by the two-step MR method. MR analyses showed that genetically predicted schizophrenia was associated with an increased risk of MDD (IVW odds ratio [OR] = 1.137 [95% CI 1.095, 1.181]). Reversely, MDD was also associated with an increased risk of schizophrenia (IVW OR = 1.323 [95% CI 1.118, 1.565]). The mediation analysis via the two-step MR method revealed that the causal effect of schizophrenia on MDD was partly mediated by the Townsend deprivation index with a proportion of 10.27%, but no significant mediation effect was found of SES on the causal effect of MDD on schizophrenia. These results suggest a robust bidirectional causal effect between schizophrenia and MDD. Patients with schizophrenia could benefit from the early and effective intervention of the Townsend deprivation index.

Denoising algorithms for obtaining the effective data of depth images affected by random noise mainly focus on the processing of gray images. These algorithms are not distinct from traditional image-processing methods, and there is no way to evaluate the effectiveness of denoising after the point cloud transformation of denoised depth images. In this paper, the principle of fractional-order integral denoising is studied in detail and inverse distance weighted interpolation is introduced into a denoising model, which is based on the G–L (Grünwald–Letnikov) fractional-order integral to construct a fractional-order integral with an inverse distance weighted denoising model. The model is used to solve the blurring problem caused by sharp changes at the edge and achieves an excellent denoising effect. By using the optimized fractional-order integral denoising operator to construct a denoising model for depth images, the results of the experiments demonstrate that the fractional-order integral of the best denoising effect achieved by the model is −0.6 ≤ ν ≤ −0.4, and the peak signal-to-noise ratio is improved from +6 to +13 dB. In the same condition, median denoising has a distortion of −30 to −15 dB. The depth image that has been denoised is converted into an image of point clouds, and subjective evaluation indicates that the noise is effectively removed. On the whole, the results demonstrate that the fractional-order integral denoising operator with inverse distance weight shows the high efficiency and the outstanding effect in removing noise from depth images while maintaining the image related to the edge and texture information.

Exploring advanced thermoelectric materials, especially flexible thermoelectric fibers, is promising for wearable devices. The thermoelectric properties of these fibers are evaluated using the figure of merit ZT value. However, there is a lack of empirical research on the properties of microscale thermoelectric fibers, necessitating the development of precise measurement methods. In addition, since the properties of micro- and nanofiber materials can be affected by the microstructure, separate measurements of electrical conductivity, Seebeck coefficient, and thermal conductivity before calculating the ZT values can lead to large errors in the final calculations. In this study, Bi2Te2.7Se0.3 thermoelectric fibers are prepared and measured by using a thermally drawn method and an in situ method, respectively. The in situ measurements are carried out using a self-developed instrument capable of measuring temperatures from room temperature up to 1,200 K, suitable for sample sizes ranging from micro- to nanoscale. The uncertainty of the measurement exhibits less than 6.36%. The results indicate that the thermal drawing process influences crystal growth, enhancing the Seebeck coefficient and reducing electrical conductivity and thermal conductivity. Moreover, the accuracy of the measurement method is verified by pure Pt wire. The integrated in situ measurement effectively reduces experimental errors due to sample differences when calculating parameters for multiple samples measured individually, and the maximum error that can be reduced is 19.5%. This research contributes a practical measurement method of thermoelectric fibers and advances the development of wearable thermoelectric devices.

Background and purposeOff-clamp robot-assisted partial nephrectomy (Offc-RAPN) is a technically challenging procedure that can effectively avoid renal ischemia owing to the absence of hilar vessel preparation and clamping. However, data on the learning curve (LC) for this technique are limited. The purpose of this study was to assess the LC of Offc-RAPN and compare the perioperative outcomes between different learning phases.MethodsThis retrospective study included 50 consecutive patients who underwent purely Offc-RAPN between January 2022 and April 2023. Multidimensional cumulative sum (CUSUM) analysis method was used to assess LC. Spearman's correlation and LOWESS analysis were performed to analyze the continuous variables of perioperative outcomes. Baseline characteristics and perioperative outcomes were compared using χ2-test, t-test and U-test.ResultsCUSUM analysis identified two LC phases: phase I (the first 24 cases) and phase II (the subsequent 26 cases). Phase II showed significant reductions in mean operative time (133.5 vs. 115.31 min; p = 0.04), mean console time (103.21 vs. 81.27 min; p = 0.01), and mean postoperative length of stay (5.33 vs. 4.30 days; p = 0.04) compared to phase I. However, no significant differences were observed in other perioperative outcomes or baseline characteristics between the two LC phases.ConclusionsOffc-RAPN performed by a surgeon with experience in laparoscopic and robotic surgeries achieved early proficiency in 24 cases. Moreover, Offc-RAPN alone is safe and feasible even in the initial phase of the LC for an experienced surgeon.

We analyzed the clinical characteristics and prognosis of early pregnancy-associated breast cancer (PABC). In this matched case-control study, the PABC patients and non-PABC patients were recruited from Hebei Breast Disease Treatment Center in a ratio of 1:2, involving 40 PABC patients (10 pregnant women, 30 postpartum women) diagnosed between January 2011 and December 2017, and 80 non-PABC patients with matched tumor staging, age (±3 years), and year of diagnosis (± 2 years). The pathological characteristics, disease-free survival (DFS), and overall survival (OS) were compared between the groups. The first gravidity age in PABC patients was significantly older than that of non-PABC patients (26.8 years vs. 23.5 years, P

In semi-steelmaking, the bottom-blown O2–CaO process is employed to achieve high efficiency dephosphorization. However, there is little research on the requirements for smelting parameters. In this paper, the mechanism changes of temperature effect on dephosphorization are studied by the industrial tests in a 300 t dephosphorization converter. Firstly, the influences of temperature on the equilibrium constant (KP), CaO melting ratio (K) and slag viscosity (ƞ) were analyzed. KP decreases, K increases and ƞ decreases to stability with increasing temperature. The dephosphorization effect is better than the conventional process' even at higher ƞ, because the CaO powder has melted and P is mostly removed during the process of fire-spot zone upward floatation. Hence, the requirements for slagging with good fluidity are reduced. Then, the relationships between the three factors and phosphorus partition between slag and hot metal (LP) were investigated by the correlation analysis. The importance order in the bottom-blown O2–CaO process is: lgKP＞η, it demonstrates that the melting of CaO powder doesn't affect dephosphorization. While the influence order in the conventional process is: K＞η＞lgKP. Finally, we summarize the dephosphorization reaction in the bottom-blown O2–CaO process includes transient reaction accounts for approximately 86% and continuous reaction with about 14%. The process of CaO upward floatation plays major roles in dephosphorization. Based on above results, the temperature requirement of molten steel for dephosphorization becomes lower. The industrial tests show the optimum temperature range for dephosphorization decreases from 1593-1603 K to 1573–1583 K, which validate the conclusion.

Immunotherapy has shown promising clinical results in clear cell renal cell carcinoma (ccRCC), but low clinical target response rates due to dysfunction of the major histocompatibility complex (MHC) and an inhibitory tumor immune microenvironment (TIME) have largely limited the associated clinical benefits. In the present study, we explored the feasibility of enhancing tumor-specific-MHC-II-HLA-DRA expression, counteracting the TIME’s suppressive effects, thereby improving the sensitivity of immune checkpoint inhibitor (ICI) therapy from the standpoint of cuproptosis. Immunohistochemical staining and in vitro experiments validated the expression of HLA-DRA in ccRCC and its positive impact on ICI therapy. Subsequently, we observed that cuproptosis upregulated HLA-DRA expression in a dose-dependent manner, further confirming the link between cuproptosis and HLA-DRA. In vivo experiments showed that cuproptosis increased the sensitivity to ICI treatment, and implementing cuproptosis alongside anti-PD-1 treatment curtailed tumor growth. Mechanistically, cuproptosis upregulates HLA-DRA expression at the transcriptional level in a dose-dependent manner by inducing the production of reactive oxygen species; high levels of HLA-DRA promote the expression of chemokines CCL5, CXCL9, and CXCL10 in the TIME, inhibiting the development of a pro-tumor microenvironment by promoting the infiltration of CD4+T and CD8+T cells, thereby synergizing ICI therapy and exerting anti-tumor effects. Taken together, this work highlights the role of cuproptosis in mediating TIME remodeling and synergistic immunotherapy, providing new evidence that cuproptosis can evoke effective anti-tumor immune responses.

Abstract Background and objective Protease-activated receptor 1 (PAR1) is crucial in individuals with acute myocardial infarction (AMI). The continuous and prompt PAR1 activation mainly dependent on PAR1 trafficking is essential for the role of PAR1 during AMI in which cardiomyocytes are in hypoxia. However, the PAR1 trafficking in cardiomyocytes specially during the hypoxia is still unclear. Methods and result A rat AMI model was created. PAR1 activation with thrombin-receptor activated peptide (TRAP) had a transient effect on cardiac function in normal rats but persistent improvement in rats with AMI. Cardiomyocytes from neonatal rats were cultured in a normal CO2 incubator and a hypoxic modular incubator chamber. The cells were then subjected to western blot for the total protein expression and staining with fluorescent reagent and antibody for PAR1 localization. No change in total PAR1 expression following TRAP stimulation was observed; however, it led to increased PAR1 expression in the early endosomes in normoxic cells and decreased expression in the early endosomes in hypoxic cells. Under hypoxic conditions, TRAP restored the PAR1 expression on both cell and endosomal surfaces within an hour by decreasing Rab11A (8.5-fold; 179.93 ± 9.82% of the normoxic control group, n = 5) and increasing Rab11B (15.5-fold) expression after 4 h of hypoxia. Similarly, Rab11A knockdown upregulated PAR1 expression under normoxia, and Rab11B knockdown downregulated PAR1 expression under both normoxic and hypoxic conditions. Cardiomyocytes knocked out of both Rab11A, and Rad11B lost the TRAP-induced PAR1 expression but still exhibited the early endosomal TRAP-induced PAR1 expression under hypoxia. Conclusions TRAP-mediated activation of PAR1 in cardiomyocytes did not alter the total PAR1 expression under normoxic conditions. Instead, it triggers a redistribution of PAR1 levels under normoxic and hypoxic conditions. TRAP reverses the hypoxia-inhibited PAR1 expression in cardiomyocytes by downregulating Rab11A expression and upregulating Rab11B expression.

In response to the limitations observed in the Intelligent Water Droplet (IWD) algorithm for path planning, including its weak problem-solving capability and susceptibility to local optimization, this paper presents an enhanced Intelligent Water Droplet algorithm. The improved algorithm incorporates the distance factor between nodes and the target point into the original algorithm’s path probability, enhancing its problem-solving prowess and expediting algorithmic convergence. Simultaneously, a roulette-based probability selection method is introduced to circumvent local optimization during the solution process. Additionally, the algorithm is coupled with the Floyd algorithm to refine the planned path, reducing the number of inflection points to align with the motion characteristics of the inspection robot. Simulation results underscore the effectiveness of the enhanced Intelligent Water Droplet algorithm in mitigating the impact of local optimization in path planning. In comparison to the original IWD algorithm, the optimized path exhibits a 17.42% reduction in length, a 58.3% decrease in the minimum number of iterations required for path convergence, and a 36.3% reduction in the number of path inflection points. Furthermore, the total path length is decreased by 7.7% following path optimization via the Floyd algorithm.

Abstract Frequency diversity array (FDA) radar provides more degrees of freedom and can generate range‐angle‐time‐dependent beampattern. Furthermore, based on the receiver's multi‐input multi‐output (MIMO) scheme, the frequency diversity array‐multi‐input multi‐output (FDA‐MIMO) radar can develop a range‐dependent but time‐independent beampattern. Taking advantage of this feature, this article improves the signal‐to‐interference‐plus‐noise ratio and the effective suppression of interference by jointly optimising the transmit beamspace and the receive filter. Since the optimisation problem is a non‐convex problem with constant energy constraints, we transform it into two convex subproblems with restrictions. The whole algorithm follows the alternate optimisation over receive filter and transmit beamspace. By simulating challenging scenarios, we confirmed the effectiveness and superiority of the method.

Abstract Chromosome rearrangements play an important role in the speciation of plants and animals, and the recognition of chromosome rearrangement patterns is helpful to elucidate the mechanism of species differentiation at the chromosome level. However, the existing chromosome rearrangement recognition methods have some major limitations, such as low quality, barriers to parental selection, and inability to identify specific rearrangement patterns. Based on the whole genome protein sequences, we constructed the combined figure according to the slope of the collinear fragment, the number of homologous genes, the coordinates in the top left and bottom right of the collinear fragment. The standardized combination figure is compared with the four standard pattern figures, and then combined with the information entropy analysis strategy to automatically classify the chromosome images and identify the chromosome rearrangement pattern. This paper proposes an automatic karyotype analysis method EntroCR (intelligent recognition method of chromosome rearrangement based on information entropy), which integrates rearrangement pattern recognition, result recommendation and related chromosome determination, so as to infer the evolution process of ancestral chromosomes to the existing chromosomes. Validation experiments were conducted using whole-genome data of Gossypium raimondii and Gossypium arboreum, Oryza sativa and Sorghum bicolor. The conclusions were consistent with previous results. EntroCR provides a reference for researchers in species evolution and molecular marker assisted breeding as well as new methods for analyzing karyotype evolution in other species.

Abstract Background Circulating tumor DNA (ctDNA) has been proven to be a promising tumor-specific biomarker in solid tumors, but its clinical utility in risk stratification and early prediction of relapse for diffuse large B cell lymphoma (DLBCL) has not been well explored. Methods Here, using a lymphoma-specific sequencing panel, we assessed the prognostic and predictive utilities of ctDNA measurements before, during, and after first-line therapy in 73 Chinese DLBCL patients. Results The pretreatment ctDNA level serving as an independent prognostic factor for both progression-free survival (PFS, adjusted HR 2.47; p = 0.004) and overall survival (OS, adjusted HR 2.49; p = 0.011) was confirmed in our cohort. Furthermore, the patients classified as molecular responders who presented a larger decrease in ctDNA levels after the initial two treatment cycles had more favorable PFS (unreached vs. 6.25 months; HR 5.348; p = 0.0015) and OS (unreached vs. 25.87; HR 4.0; p = 0.028) than non-responders. In addition, interim ctDNA clearance may be an alternative noninvasive method of positron emission tomography and computed tomography (PET-CT) for predicting better PFS (HR 3.65; p = 0.0033) and OS (HR 3.536; p = 0.016). We also demonstrated that posttreatment ctDNA was a sensitive indicator for detecting minimal residual disease (MRD) in patients with a high risk of recurrence (HR 6.471; p = 0.014), who were otherwise claimed to achieve radiographic CR (complete remission). Conclusions CtDNA is a promising noninvasive tool for prognosis prediction, response assessment, and early relapse prediction of first-line treatment in DLBCL patients.

Abstract Background Approximately 90% of renal malignancies are RCCs (renal cell carcinomas), and the primary subtype in histology is ccRCC (clear cell RCC). In recent years, pyroptosis has been considered a kind of inflammation-related programmed cell death that participates in the invasion, metastasis, and proliferation of tumour cells, thereby influencing tumour prognosis. Nonetheless, the expression level of pyroptosis-associated genes in RCCs and their relationship with prognosis remain obscure. Results In our research, 44 regulators of pyroptosis that were differentially expressed between normal kidney and ccRCC tissues were identified. ccRCC cases were categorized into 2 subgroups according to prognostic-related DEGs (differentially expressed genes), and there was a significant difference in OS (overall survival) between them. The prognostic value of pyroptosis-associated genes was assessed as a signature based on a cohort from TCGA (The Cancer Genome Atlas). Following Cox regression with DEGs and LASSO (least absolute shrinkage and selection operator), a 6-gene signature was established, and all ccRCC cases in the cohort from TCGA were categorized into an LR (low-risk) or HR (high-risk) group (P

BackgroundThe clinical benefits of endovascular treatment in renal artery stenosis (RAS) remain controversial. This study used an intraoperative renal perfusion imaging technique, called flat-panel detector parenchymal blood volume imaging (FD-PBV), to observe the change in renal perfusion after endovascular treatment in RAS.Materials and methodsIn a prospective, single-center study, we assigned 30 patients with atherosclerotic RAS who underwent endovascular treatment between March 2016 and March 2021. The preoperative and postoperative results of renal perfusion, blood pressure, and renal function, were compared.ResultsBoth median kidney volume (p

The performance of laser beams in tracking Lissajous scan trajectories is severely limited by beam jitter. To enhance the performance of fast steering mirror (FSM) control in tracking Lissajous scan trajectories, this paper proposed a fractional order active disturbance rejection controller (FOADRC) and verified its effectiveness in improving system scanning tracking accuracy. A dynamic mathematical model of a fast steering mirror was studied, and the design of parameters for the control mode of the closed-loop system was determined. A reduced-order linear active disturbance rejection controller suitable for FSM systems was designed, and the corresponding fractional-order proportional differentiation (FOPD) controller was determined according to the mathematical model. The use of the designed controller enabled high-performance tracking of high-frequency Lissajous scanning curves (X-axis 500 Hz, Y-axis 350 Hz) and met the need for high-frequency repetitive scanning. The controller has the characteristics of simple implementation and low computational complexity and is suitable for closed-loop control applications in engineering.

Transmembrane emp24 domain (TMED) proteins have been extensively studied in mammalian embryonic development, immune regulation, and signal transduction. However, their role in insects, apart from Drosophila melanogaster, remains largely unexplored. Our previous study demonstrated the abundant expression of BmTMED6 across all stages and tissues of the silkworm. In this study, we investigate the function of BmTMED6 in reproduction. We observe significant differences in the expression of BmTMED6 between male and female silkworms, particularly in the head and fatboby, during the larval stage. Furthermore, qRT-PCR and WB analysis reveal substantial variation in BmTMED6 levels in the ovaries during pupal development, suggesting a potential association with silkworm female reproduction. We find that reducing TMED6 expression significantly decreases the number of eggs laid by female moths, leading to an accumulation of unlaid eggs in the abdomen. Moreover, downregulation of BmTMED6 leads to a decrease in the expression of BmDop2R1 and BmDop2R2, while overexpression of BmTMED6 in vitro has the opposite effect. These indicate that BmTMED6 plays a role in oviposition in female moths, potentially through the dopamine signaling pathway. This study provides a new regulatory mechanism for female reproduction in insects.

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.

Abstract Background and objective Emerging evidence has proven the robust role of tumor mutation burden (TMB) and immune cell infiltration (ICI) in cancer immunotherapy. However, the precise effect of TMB and ICI on clear cell renal cell carcinoma (ccRCC) remains elusive and merits further investigation. Therefore, we aim to identify the TMB-related genes in predicting prognosis and to explore the potential mechanisms of the identified Interleukin 20 receptor subunit beta (IL20RB) in ICI in ccRCC. Method The relative information of patients with ccRCC was obtained from The Cancer Genome Atlas database (TCGA). Immune-related genes were downloaded from the Immunology Database and Analysis Portal database. Cox regression analysis was used to identify prognosis-related immune genes for ccRCC. The relationship of IL20RB expression levels with clinicopathological parameters was analyzed using the “limma” and “survival” packages. Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) databases were used as external validation. Quantitative Real-time PCR (qRT-PCR) and western blots were used to validate the expression levels of IL20RB in tumor cells. Cell counting kit-8 (CCK-8) assay and colony formation assay were used to examine the effect of IL20RB on the viability of ccRCC cells. Gene set enrichment analysis (GSEA) was introduced for the analysis of IL20RB-related signaling pathways. Tumor Immune Estimation Resource (TIMER) and Tumor and Immune System Interaction Database (TISIDB) were utilized to determine the correlation of IL20RB expression levels with tumor-infiltrating immune cells (TIICs). Results IL20RB was significantly overexpressed in different ccRCC tissues and cells. High IL20RB expression in ccRCC patients was associated with short overall survival, high tumor grade, and advanced TNM stage. After knockdown of IL20RB with small interfering RNA (siRNA) technology, ccRCC cells’ proliferation was significantly attenuated. Moreover, overexpression of IL20RB could increase the infiltration level of several immune cells, especially T follicular helper cells (Tfh), and overexpressed Tfh cells were correlated with poor prognosis in ccRCC. Conclusions IL20RB may function as an immune-associated therapeutic target for it determines cancer progression and regulates immune cell infiltration in ccRCC.

Abstract The frequency diverse array (FDA) can provide degrees of freedom in both the range and angle domains by controlling the frequency offset across adjacent elements. Nevertheless, the time‐variant problem affects the analysis and application of FDA, and there is a notable lack of research on the receivers of multicarrier FDA. In this paper, a novel perspective to understand the factor of time parameter is proposed. Furthermore, combined with the multiple input and multiple output scheme, two multicarrier FDA radar receivers are proposed to obtain the time‐invariant and dot‐shaped beampattern. Numerical simulations demonstrate the validity of theoretical results.

Induced molting enables laying hens to relax, restore energy and prolong the laying hen cycle, resolving problems such as poor egg quality and minimizing economic losses caused by rising global feeding costs. However, traditional molting methods may disrupt gut microflora and promote potential pathogens infections. This study used a customized additive with a mixture of probiotics and vitamins to induce molting and examine the cecal microbiota post molting. A total of two hundred 377 day-of-ISA Brown laying hens were randomly assigned to four groups: non-molt with basal diet (C), 12-day feeding restriction (FR) in earlier-molting (B), feed again to 27.12% egg production in middle-molting (A) and reach second peak of egg production over 81.36% in post-molting (D). Sequencing 16S rRNA to analyze cecal microbial composition revealed that there is no significant change in bacterial community abundance post-molting. In contrast to group C, the number of potentially harmful bacteria such as E. coli and Enterococcus was not found to increase in groups B, A, or D. This additive keeps cecal microbiota diversity and community richness steady. In cecal contents, hens in group B had lower Lactobacillus, Lachnospiraceae and Prevotellaceae (vsC, A, and D), no significant differences were found between post-molting and the non-molting. Furthermore, cecal microbiota and other chemicals (antibodies, hormones, and enzymes, etc.) strongly affect immunological function and health. Most biochemical indicators are significantly positively correlated with Prevotellaceae, Ruminococcaceae and Subdoligranulum, while negatively with Phascolarctobacterium and Desulfovibrio. In conclusion, the additive of probiotics and vitamins improved the cecal microbiota composition, no increase in the associated pathogenic microbial community due to traditional molting methods, and enhances hepatic lipid metabolism and adaptive immunological function, supporting their application and induced molting technology in the poultry breeding industry.

Background and purposeUrinary incontinence is one of the common side effects of robot-assisted radical prostatectomy (RARP). Here, we described the modified Hood technique for single-port RARP (sp-RARP) and assessed the interest of this new technique for early continence recovery.MethodsWe retrospectively reviewed 24 patients who underwent sp-RARP modified hood technique from June 2021 to December 2021. The pre-and intraoperative variables, postoperative functional and oncological outcomes of patients were collected and analyzed. The continence rates were estimated at 0 day, 1 week, 4 weeks, 3 months and 12 months after catheter removal. Continence was defined as wearing no pad over a 24 h period.ResultsMean time of operation and estimated blood loss were 183 min and 170 ml, respectively. The postoperative continence rates at 0 day, 1 week, 4 weeks, 3 months and 12 months after catheter removal were 41.7%, 54.2%, 75.0%, 91.7% and 95.8%, respectively. There were two patients who detected positive surgical margins and no patients observed complications requiring further treatment.ConclusionThe modified hood technique is a safe and feasible method that provides better outcomes in terms of early return of continence, without increasing estimated blood loss and compromising oncologic outcomes.

Background: Eczema is a common chronic relapsing inflammatory skin disease, which is characterized by intense itching. Acupuncture can be effective for eczema, and it is thus regarded as a common complementary treatment. Objective: The intention of this overview is to methodically appraise and synthesize evidence about systematic reviews/meta-analyses (SRs/MAs) on acupuncture in eczema. Methods: We searched for SRs/MAs of acupuncture with eczema in eight databases. We evaluated the methodological quality by Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR-2), the reporting quality with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020 Checkist), and the evidence quality according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Results: A total of 7 SRs/MAs were included. According to AMSTAR-2, all the SRs/MAs included were categorized as critically low-quality. According to the PRISMA 2020 checklist, none of the reviews completed all the 27 items, thus their compliance was relatively weak. On the base of GRADE system, 2 of the 12 outcomes were rated as moderate, and 5 outcomes were rated as low-quality, while the others were regarded as very low-quality. Conclusion: Compared with the control group, the included reviews of the acupuncture group were more effective and safer; however, the conclusion should be treated cautiously because the quality of evidence was not high enough to support it. In order to improve the quality, more rigorous, standardized, and comprehensive SRs/MAs need designing in the future.

Abstract This paper aims to investigate the efficacy of circularly polarized light smartphones in affecting dry eye symptoms and asthenopia through a comparison with linearly polarized smartphones. One hundred twenty participants were randomly divided into four groups. Dry eye and asthenopia symptoms were evaluated using the Ocular Surface Disease Index (OSDI), Computer Vision Syndrome Scale 17 (CVSS17), Convergence Insufficiency Symptom Survey (CISS), and visual analogue scale (VAS). Objective ocular examinations were assessed by confusion flicker frequency (CFF), tear meniscus height (TMH), noninvasive break‐up time (NIBUT), conjunctiva redness, fluorescein tear break‐up time (FTBUT), corneal fluorescein staining, and the Schirmer I test. Tests were performed before and after a reading task. Subjective evaluations including the OSDI, CVSS17, and CISS were all significantly increased after reading on a linearly polarized smartphone, whereas no change was observed in the circular polarization groups in both light and dark environments. A significantly enlarged VAS was shown in all of the four groups, but a significant increase in ΔVAS only appeared in the linear polarization groups. There were significant decreases in TMH, NIBUT, conjunctiva redness, FTBUT, and CFF after reading on a linearly polarized smartphone but the circularly polarized smartphone had lesser effects on these parameters. Our study indicated that reading on linearly polarized smartphones may cause dry eye disorder, asthenopia, and ocular discomforts, whereas circularly polarized smartphones appears to minimize these adverse effects on eye dryness and visual fatigue in light and dark environments.

With the widespread application of multi-axis machining in the industrial manufacturing, aerospace, and military equipment sectors, the demand for machining ultra-precision components has been steadily increasing. Contour errors directly impact the quality of machined parts. In conventional multi-axis motion control systems based on cross-coupling, it is conventionally assumed that all individual axes are of equal significance during machine processing. However, in practical machining scenarios, diverse machining trajectories and accuracy requirements give rise to distinct control necessities for each axis. This complication leads to challenges in ensuring a consistent single-plane contour, thereby constraining the elevation of the overall contour accuracy. To address this issue, this study proposes a multi-axis contour error suppression method based on composite hierarchical control. The approach advocated in this paper initially ensures the precision of single-axis position control through the development of an advanced S-shaped function-based sliding-mode disturbance observer. Building on this foundation, the three-dimensional spatial contour is segregated into upper and lower layers. Subsequently, dedicated fuzzy PID cross-coupling controllers are devised for each layer. The experimental outcomes substantiate that in comparison to conventional cross-coupling control methods, the method introduced in this study, rooted in composite hierarchical control, not only guarantees the accuracy of single-plane contours but also further enhances the overall contour precision.

Image super-resolution (SR) techniques can improve the spatial resolution of remote sensing images to provide more feature details and information, which is important for a wide range of remote sensing applications, including land use/cover classification (LUCC). Convolutional neural networks (CNNs) have achieved impressive results in the field of image SR, but the inherent localization of convolution limits the performance of CNN-based SR models. Therefore, we propose a new method, namely, the dilated Transformer generative adversarial network (DTGAN) for the SR of multispectral remote sensing images. DTGAN combines the local focus of CNNs with the global perspective of Transformers to better capture both local and global features in remote sensing images. We introduce dilated convolutions into the self-attention computation of Transformers to control the network’s focus on different scales of image features. This enhancement improves the network’s ability to reconstruct details at various scales in the images. SR imagery provides richer surface information and reduces ambiguity for the LUCC task, thereby enhancing the accuracy of LUCC. Our work comprises two main stages: remote sensing image SR and LUCC. In the SR stage, we conducted comprehensive experiments on Landsat-8 (L8) and Sentinel-2 (S2) remote sensing datasets. The results indicate that DTGAN generates super-resolution (SR) images with minimal computation. Additionally, it outperforms other methods in terms of the spectral angle mapper (SAM) and learned perceptual image patch similarity (LPIPS) metrics, as well as visual quality. In the LUCC stage, DTGAN was used to generate SR images of areas outside the training samples, and then the SR imagery was used in the LUCC task. The results indicated a significant improvement in the accuracy of LUCC based on SR imagery compared to low-resolution (LR) LUCC maps. Specifically, there were enhancements of 0.130 in precision, 0.178 in recall, and 0.157 in the F1-score.

The transmitted beam of frequency diversity array (FDA) has the range–angle–time coupling property, which has essential applicative potential in angle deception and active anti-jamming. In this paper, the concept of time index within pulse is introduced. Firstly, the phase characteristics of FDA-transmitted signals based on the time index within pulse concept are studied. Then, the deceptive angle performance of FDA-transmitted signals is discussed. The theoretical analysis and simulation results show that the phase characteristics of the FDA signal are not related to the range, but to the time index within pulse. With the phase center as the reference point, the phase is equal as long as the time index within the pulse is the same. Angle deception and active anti-jamming can be achieved using the optimized frequency increment of each FDA.

To address the performance degradation of the conventional linear frequency modulation signal ranging method in the presence of impulse noise, this paper proposes a novel technique that integrates a sliding-window tracking differentiator (TD) with the fractional Fourier transform (FrFT) ranging method. First, the sliding-window TD filtering algorithm is used to suppress the noise in the echo. Subsequently, the filtered signal is subjected to FrFT to calculate the time delay based on the difference in the peak point positions in the fractional domain for realizing target ranging. The simulation results show that the proposed method can effectively suppress impulse noise of different intensities and achieve an accurate and robust ranging of the target.

A Geiger-mode avalanche photodiode (GM-APD) laser radar range image has much noise when the signal-to-background ratios (SBRs) are low, making it difficult to recover the real target scene. In this paper, based on the GM-APD lidar denoising model of fractional-order total variation (FOTV), the spatial relationship and similarity relationship between pixels are obtained by using a spatial kernel function and range kernel function to optimize the fractional differential operator, and a new FOTV GM-APD lidar range-image denoising algorithm is designed. The lost information and range anomalous noise are suppressed while the target details and contour information are preserved. The Monte Carlo simulation and experimental results show that, under the same SBRs and statistical frame number, the proposed algorithm improves the target restoration degree by at least 5.11% and the peak signal-to-noise ratio (PSNR) by at least 24.6%. The proposed approach can accomplish the denoising of GM-APD lidar range images when SBRs are low.

Glioblastoma is carcinogenesis of glial cells in central nervous system and has the highest incidence among primary brain tumors. Brain metastasis, such as breast cancer and lung cancer, also leads to high mortality. The available medicines are limited due to blood–brain barrier. Abnormal activation of phosphatidylinositol 3-kinases (PI3K) signaling pathway is prevalent in glioblastoma and metastatic tumors. Here, we characterized a 2-amino-4-methylquinazoline derivative XH30 as a potent PI3K inhibitor with excellent anti-tumor activity against human glioblastoma. XH30 significantly repressed the proliferation of various brain cancer cells and decreased the phosphorylation of key proteins of PI3K signaling pathway, induced cell cycle arrest in G1 phase as well. Additionally, XH30 inhibited the migration of glioma cells and blocked the activation of PI3K pathway by interleukin-17A (IL-17A), which increased the migration of U87MG. Oral administration of XH30 significantly suppressed the tumor growth in both subcutaneous and orthotopic tumor models. XH30 also repressed tumor growth in brain metastasis models of lung cancers. Moreover, XH30 reduced IL-17A and its receptor IL-17RA in vivo. These results indicate that XH30 might be a potential therapeutic drug candidate for glioblastoma migration and brain metastasis.

This paper deals with the problem of robust beamforming and target power estimation in the presence of main-lobe interference with frequency division array multiple-input multiple-output (FDA-MIMO) radar by adopting a weighting matrix at the receiving end. Based on the semi-definite programming (SDP) to solve the covariance matrix of the weighting matrix, we propose two beamforming methods and two methods for estimating the target power when the interference has a position error. Then, we propose obtaining the weighting matrix by performing singular value decomposition on the covariance matrix. We confirm the effectiveness of the developed method by comparing beam pattern and target signal estimation performance.

IntroductionCurrent detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.MethodsIn the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization.ResultsThe experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms.DiscussionIn summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.

BackgroundMucinous cystadenocarcinoma (MC) of the kidney is a rare renal epithelial tumor originating from the renal pelvic urothelium. There are only a few published reports on MC. Due to its rare and unknown tissue origin, its diagnosis is difficult which almost can be diagnosed through the pathological method.Case presentationIn this case report, we report a female patient whose chief complaint was low back pain lasting for one month. The three-dimensional computed tomography scan of the urinary system detected approximately 7 cm of a left renal cystic mass. The renal cystic mass was diagnosed as MC after robot-assisted laparoscopic radical nephrectomy. The MC originated from the kidney after completing colorectal adenocarcinoma and ovarian adenocarcinoma.ConclusionsWe reported a case of MC of the kidney which was a rare renal tumor. We not only aimed to present an unusual case of MC and review the previous literature on its pathology and differential diagnosis, but also used new method to treat this type of tumor.

This study was conducted to evaluate the effect of pyridoxine on the development of hair follicles in Rex rabbits and the underlying molecular mechanism. Two hundred 3-month-old Rex rabbits were randomly divided into 5 groups and fed diets supplemented with 0, 5, 10, 20, or 40 mg/kg pyridoxine. The hair follicle density on the dorsal skin and the gene and protein expression levels of components of the phosphoinositide 3-kinase (PI3K)/protein kinase B (PKB or Akt), Wnt, Notch and bone morphogenetic protein (BMP) signalling pathways were measured. In addition, free hair follicles were isolated from Rex rabbits and cultured with pyridoxine in vitro to measure hair shaft growth. Furthermore, dermal papilla cells (DPC) were isolated from the skin of Rex rabbits and cultured with pyridoxine in vitro to measure the gene and protein expression levels of components of the PI3K/Akt, Wnt, Notch and BMP signalling pathways. The results showed that the addition of dietary pyridoxine significantly increased the total follicle density, secondary follicle density, and secondary-to-primary ratio (S/P, P